This invention relates to flow cytometry systems and methods. More specifically, this invention relates to systems and methods for analyzing blood samples to identify, classify, and/or quantify rare cellular events.
Examples of rare cellular events include: Circulating Tumor Cells (CTCs), Circulating Stem Cells (CSCs), fetal cells in maternal blood, circulating liver or kidney cells, etc. CTCs and CSCs are currently being used in clinical settings to monitor disease progression, effectiveness of treatment, relapse, and remission. Fetal cells in maternal blood are sought to perform minimally invasive genetic clinical tests on the developing fetus, without incurring significant health risks for both the fetus and the mother (e.g., in amniocentesis). Such clinically relevant cell populations, however, when they are present in peripheral blood at all, can be in exceedingly low concentrations (e.g., measured in thousands, hundreds, or even single digits of cells per milliliter). Since the natural concentration of healthy cells in blood is in the millions per milliliter (white blood cells) to billions per milliliter (red blood cells), identification and counting of rare cells requires a sensitivity of as much as 1 in 109, rare cellular events per normal cellular events.
Prior analysis systems typically involve a complex, multi-part sample preparation process, which, combined with the detection approach used, has yield issues (e.g., losses of the cells of interest). Further, even if red blood cells are lysed in a sample preparation step prior to analysis, identification and throughput times for the detection and analysis of rare cellular events is resource-consuming and inefficient. Prior systems, for example, typically take hours to deliver results.
The systems and methods presented herein provide a reliable, high-throughput, high-yield, low-cost, cellular analysis system for the identification, counting, classification, and optionally segregation of rare cellular events of interest.